Agent skill · design visual · aiskillstore
scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Why this skill is useful
Provides specific commands and styles for creating publication-ready figures that adhere to journal standards, which the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, plotly, seaborn installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Scientific Visualization Overview Scientific visualization transforms data into clear, accurate figures for publication. Create journal-ready plots with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. Export as PDF/EPS/TIFF using matplotlib, seaborn, and plotly for manuscripts. When to Use This Skill This skill should be used when: Creating plots or visualizations for scientific manuscripts Preparing figures for journal submission (Nature, Science, Cell, PLOS, etc.) Ensuring figures are colorblind-friendly and accessible Making multi-panel figures with consistent styling Exporting figures at correct resolution and format Following specific publication guidelines Improving existing figures to meet publication standards Creating figures that need to work in both color and grayscale Quick Start Guide Basic Publication-Quality Figure Using Pre-configured Styles Apply journal-specific styles using the matplotlib style files in assets/: Quick Start with Seaborn For statistical plots, use seaborn with publication styling: Core Principles and Best Practices 1. Resolution and File Format Critical requirements (detailed in references/publicationguidelines.md): Raster images (photos, microscopy): 300-600 DPI Line art (graphs, plots): 600-1200 DPI or vector format Vector formats (preferred): PDF, EPS, SVG Raster formats: TIFF, PNG (never JPEG for scientific data) Implementation: 2. Color Selection - Colorblind Accessibility Always use colorblind-friendly palettes (detailed in references/colorpalettes.md): Recommended: Okabe-Ito palette (distinguishable by all types of color blindness): For heatmaps/continuous data: Use perceptually uniform colormaps: viridis, plasma, cividis Avoid red-green diverging maps (use PuOr, RdBu, BrBG instead) Never use jet or rainbow colormaps Always test figures in grayscale to ensure interpretability. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/scientific-visualization--4eccea